Bottom Line:
To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU).Through nested cross-validation we demonstrated that our approach yielded high classification performance.The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

ABSTRACTWe present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

pone.0133337.g004: Skeleton of our automatic classification algorithm.The skeleton of the voting algorithm to determine the label of an input group.

Mentions:
Fig 4 shows a skeleton of the voting algorithm to determine the label of an input group. We implemented our algorithm using GPU (See S1 Text for detailed explanation). If the SKLD between an input tract group and its closest group in an example subject is larger than a threshold distance τd, the subject is not qualified to vote since there is no similar example group to the input group in the example subject. In addition, if the maximum number of votes among all bundles is smaller than a threshold vote number τv, then the input group is regarded as an outlier.

pone.0133337.g004: Skeleton of our automatic classification algorithm.The skeleton of the voting algorithm to determine the label of an input group.

Mentions:
Fig 4 shows a skeleton of the voting algorithm to determine the label of an input group. We implemented our algorithm using GPU (See S1 Text for detailed explanation). If the SKLD between an input tract group and its closest group in an example subject is larger than a threshold distance τd, the subject is not qualified to vote since there is no similar example group to the input group in the example subject. In addition, if the maximum number of votes among all bundles is smaller than a threshold vote number τv, then the input group is regarded as an outlier.

Bottom Line:
To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU).Through nested cross-validation we demonstrated that our approach yielded high classification performance.The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

ABSTRACTWe present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.